Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.

MOTIVATION: The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. RESULTS: In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. AVAILABILITY AND IMPLEMENTATION: Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.

[Verification of Response Uncertainty in Electrometers through Cross-Comparison with a Novel Current Source: A Comparative Study with Guidelines for Electrometers Used in Radiation Therapy Dosimeters].

BACKGROUND: A new quality assurance and control method for electrometers using a new current source, different from the method published in the guidelines for electrometers, has been reported. This current source uses dry batteries and exhibits excellent performance in terms of voltage, temperature, and time characteristics. The electrometer sensitivity coefficient can be calculated by comparing the sensitivity of one electrometer with that of another on the electrometer calibration coefficient that has been calibrated by a calibration laboratory in advance in both methods. The guideline method requires two or more sets of ionization chambers and electrometers in the facility. In contrast, our method does not use ionization chambers; therefore, the sensitivity ratio of the electrometer can be measured in any facility. This study compared the uncertainty of the electrometer sensitivity factor calculated using the new current source method (current method) with that calculated using a linear accelerator (LINAC) and ionization chambers (LINAC method) described in the electrometer guidelines. METHOD: In this study, we used a current source that we invented previously by Kawaguchi Electric Works in Japan. The sensitivity ratios of the electrometers were measured with three manufacture's electrometers. The electrometer sensitivity factor was calculated by multiplying the electrometer calibration coefficient. The ionization chamber was 30013 (PTW), and the current source was the current obtained from 10 MV TrueBeam X-rays under calibration conditions. The mean value, standard deviation, and coefficient of variation were calculated. The time required to set up the ionization chamber for calculating the sensitivity ratio of the electrometer was also measured. The accuracy was confirmed by calculating the expanded uncertainty of the electrometer sensitivity coefficients. RESULTS: The LINAC method had a maximum coefficient of variation of 0.072%. The gross time of the LINAC method was approximately 110 min. The current method had a maximum coefficient of variation of 0.0055% and took less than half the time taken by the LINAC method (35 min) because there was no waiting time for the ionization chamber to be set up and the applied voltage to stabilize under calibration conditions. The expanded uncertainties of the electrometer calibration coefficients were 0.36% and 0.36%, respectively. CONCLUSION: The new cross-comparison method for electrometer sensitivity factors using a current source is more efficient and useful than the linear accelerator method described in the guidelines; furthermore, this method ensured accuracy for quality assurance and control of electrometers.

Improving the Application of Prothrombin Time-Derived Fibrinogen Assay Through Value Transfer from the Von Clauss Method.

BACKGROUND: This study aimed to improve the accuracy of the fibrinogen (Fib) prothrombin time-derived (PT-der) method. To achieve this, a value transfer method was introduced for calibration, and its effectiveness was assessed. METHODS: The PT-der Fib assay was calibrated by pooled samples (assigned by the von Clauss method) in three different ways: 1) multipoint calibration using an automatic dilution system, 2) multipoint calibration using a manual dilution method, and 3) manual calibration with multiple concentrations. Three calibration equations (1, 2, and 3) were obtained and an optimal equation was selected by comparing the detection results of the von Clauss method with the PT-der method. Subsequently, the optimal equation was assessed for an accuracy limit, and linear analysis and reference interval verification were performed following the guidelines (EP15-A and EP6-A) issued by the CLSI. RESULTS: Compared with the other two equations (equation 1 and 2), equation 3, available from manual calibration with multiple concentrations, showed a better performance for the PT-der determination in a primary cohort (n = 208), and a good agreement (99% of the results between 1.52 and 6.30 g/L were interchangeable) was validated (n = 3226). The reference interval was also verified in almost all healthy individuals (39/40). However, the discrep-ancy between the two methods was observed in several specific conditions, such as hyperfibrinolysis. CONCLUSIONS: Manual calibration with multiple concentrations is better for the Fib PT-der method assay. As a rapid, accurate, and economical test, the performance of the Fib PT-der method has been verified and may be more applicable than before.

Proton CT on biological phantoms for x-ray CT calibration in proton treatment planning.

Objective.To present and characterize a novel method for x-ray computed tomography (xCT) calibration in proton treatment planning, based on proton CT (pCT) measurements on biological phantoms.Approach.A pCT apparatus was used to perform direct measurements of 3D stopping power relative to water (SPR) maps on stabilized, biological phantoms. Two single-energy xCT calibration curves-i.e. tissue substitutes and stoichiometric-were compared to pCT data. Moreover, a new calibration method based on these data was proposed, and verified against intra- and inter-species variability, dependence on stabilization, beam-hardening conditions, and analysis procedures.Main results.Biological phantoms were verified to be stable in time, with a dependence on temperature conditions, especially in the fat region: (-2.5 0.5) HU °C-1. The pCT measurements were compared with standard xCT calibrations, revealing an average SPR discrepancy within ±1.60% for both fat and muscle regions. In the bone region the xCT calibrations overestimated the pCT-measured SPR of the phantom, with a maximum discrepancy of about +3%. As a result, a new cross-calibration curve was directly extracted from the pCT data. Overall, the SPR uncertainty margin associated with this curve was below 3%; fluctuations in the uncertainty values were observed across the HU range. Cross-calibration curves obtained with phantoms made of different animal species and anatomical parts were reproducible with SPR discrepancies within 3%. Moreover, the stabilization procedure did not affect the resulting curve within a 2.2% SPR deviation. Finally, the cross-calibration curve was affected by the beam-hardening conditions on xCTs, especially in the bone region, while dependencies below 2% resulted from the image registration procedure.Significance.Our results showed that pCT measurements on biological phantoms may provide an accurate method for the verification of current xCT calibrations and may represent a tool for the implementation of a new calibration method for proton treatment planning.

DNA-based ForceChrono probes for deciphering single-molecule force dynamics in living cells.

Understanding cellular force transmission dynamics is crucial in mechanobiology. We developed the DNA-based ForceChrono probe to measure force magnitude, duration, and loading rates at the single-molecule level within living cells. The ForceChrono probe circumvents the limitations of in vitro single-molecule force spectroscopy by enabling direct measurements within the dynamic cellular environment. Our findings reveal integrin force loading rates of 0.5-2 pN/s and durations ranging from tens of seconds in nascent adhesions to approximately 100 s in mature focal adhesions. The probe's robust and reversible design allows for continuous monitoring of these dynamic changes as cells undergo morphological transformations. Additionally, by analyzing how mutations, deletions, or pharmacological interventions affect these parameters, we can deduce the functional roles of specific proteins or domains in cellular mechanotransduction. The ForceChrono probe provides detailed insights into the dynamics of mechanical forces, advancing our understanding of cellular mechanics and the molecular mechanisms of mechanotransduction.

Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry-Mass Spectrometry.

Ion mobility spectrometry (IMS) is a gas-phase analytical technique that separates ions with different sizes and shapes and is compatible with mass spectrometry (MS) to provide an additional separation dimension. The rapid nature of the IMS separation combined with the high sensitivity of MS-based detection and the ability to derive structural information on analytes in the form of the property collision cross section (CCS) makes IMS particularly well-suited for characterizing complex samples in -omics applications. In such applications, the quality of CCS from IMS measurements is critical to confident annotation of the detected components in the complex -omics samples. However, most IMS instrumentation in mainstream use requires calibration to calculate CCS from measured arrival times, with the most notable exception being drift tube IMS measurements using multifield methods. The strategy for calibrating CCS values, particularly selection of appropriate calibrants, has important implications for CCS accuracy, reproducibility, and transferability between laboratories. The conventional approach to CCS calibration involves explicitly defining calibrants ahead of data acquisition and crucially relies upon availability of reference CCS values. In this work, we present a novel reference-free approach to CCS calibration which leverages trends among putatively identified features and computational CCS prediction to conduct calibrations post-data acquisition and without relying on explicitly defined calibrants. We demonstrated the utility of this reference-free CCS calibration strategy for proteomics application using high-resolution structures for lossless ion manipulations (SLIM)-based IMS-MS. We first validated the accuracy of CCS values using a set of synthetic peptides and then demonstrated using a complex peptide sample from cell lysate.

A portable dry film FTIR instrument for industrial food and bioprocess applications.

The main objective of this study was to design, build, and test a compact, multi-well, portable dry film FTIR system for industrial food and bioprocess applications. The system features dry film sampling on a circular rotating disc comprising 31 wells, a design that was chosen to simplify potential automation and robotic sample handling at a later stage. Calibration models for average molecular weight (AMW, 200 samples) and collagen content (68 samples) were developed from the measurements of industrially produced protein hydrolysate samples in a controlled laboratory environment. Similarly, calibration models for the prediction of lactate content in samples from cultivation media (59 samples) were also developed. The portable dry film FTIR system showed reliable model characteristics which were benchmarked with a benchtop FTIR system. Subsequently, the portable dry film FTIR system was deployed in a bioprocessing plant, and protein hydrolysate samples were measured at-line in an industrial environment. This industrial testing involved building a calibration model for predicting AMW using 60 protein hydrolysate samples measured at-line using the portable dry film FTIR system and subsequent model validation using a test set of 26 samples. The industrial calibration in terms of coefficient of determination (R2 = 0.94), root mean square of cross-validation (RMSECV = 194 g mol-1), and root mean square of prediction (RMSEP = 162 g mol-1) demonstrated low prediction errors as compared to benchtop FTIR measurements, with no statistical difference between the calibration models of the two FTIR systems. This is to the authors' knowledge the first study for developing and employing a portable dry film FTIR system in the enzymatic protein hydrolysis industry for successful at-line measurements of protein hydrolysate samples. The study therefore suggests that the portable dry film FTIR instrument has huge potential for in/at-line applications in the food and bioprocessing industries.

Temporally Local Weighting-Based Phase-Locked Time-Shift Data Augmentation Method for Fast-Calibration SSVEP-BCI.

Various training-based spatial filtering methods have been proposed to decode steady-state visual evoked potentials (SSVEPs) efficiently. However, these methods require extensive calibration data to obtain valid spatial filters and temporal templates. The time-consuming data collection and calibration process would reduce the practicality of SSVEP-based brain-computer interfaces (BCIs). Therefore, we propose a temporally local weighting-based phase-locked time-shift (TLW-PLTS) data augmentation method to augment training data for calculating valid spatial filters and temporal templates. In this method, the sliding window strategy using the SSVEP response period as a time-shift step is to generate the augmented data, and the time filter which maximises the temporally local covariance between the original template signal and the sine-cosine reference signal is used to suppress the temporal noise in the augmented data. For the performance evaluation, the TLW-PLTS method was incorporated with state-of-the-art training-based spatial filtering methods to calculate classification accuracies and information transfer rates (ITRs) using three SSVEP datasets. Compared with state-of-the-art training-based spatial filtering methods and other data augmentation methods, the proposed TLW-PLTS method demonstrates superior decoding performance with fewer calibration data, which is promising for the development of fast-calibration BCIs.

A nano-level assay of tizanidine using the fluorogenic character of benzofurazan derivative: Application to plasma, tablets, and content homogeneity evaluation.

People frequently administer Tizanidine (TIZ) to treat spasticity resulting from diseases like multiple sclerosis or spinal cord injuries. It also helps prevent muscle spasms. It helps to relax and release tense and stiff muscles by inhibiting specific nerve signals in the brain and spinal cord. The technique employed in this study made use of the unique ability of benzofurazan to confer fluorescent character when reacted with TIZ at specific conditions. This fluorogenic property was harnessed to evolve a remarkably sensitive, affordable, and selective method to quantify TIZ. The resulting yellow fluorescent product was observedat a wavelength beam of 532.9 nm, and an excitation wavelength beam of 474.9 nm was applied. By looking at the response across the TIZ concentration, the calibration chart's linearity was assessed in the range of 40-500 ng/mL. By computation, the approach's detection level (LOD) was determined to be 11.9 ng/mL, while the quantitation level was approximated to be 36 ng/mL. All pertinent factors impacting the strategy's efficacy were thoroughly inspected and adjusted accordingly. The proposed strategy was validated following the guidelines outlined by the ICH. The outcomes confirmed the method's capability for the accurate quantifying of TIZ in tablets, spiked plasma, and pharmaceutical assessing content uniformity.

Quantification of antibiotics in food by octahedral gold-silver nanocages-based SERS sensor coupling multivariate calibration.

The abuse of antibiotics has caused gradually increases drug-resistant bacterial strains that pose health risks. Herein, a sensitive SERS sensor coupled multivariate calibration was proposed for quantification of antibiotics in milk. Initially, octahedral gold-silver nanocages (Au@Ag MCs) were synthesized by Cu2O template etching method as SERS substrates, which enhanced the plasmonic effect through sharp edges and hollow nanostructures. Afterwards, five chemometric algorithms, like partial least square (PLS), uninformative variable elimination-PLS (UVE-PLS), competitive adaptive reweighted sampling-PLS (CARS-PLS), random frog-PLS (RF-PLS), and convolutional neural network (CNN) were applied for TTC and CAP. RF-PLS performed optimally for TTC and CAP (Rc = 0.9686, Rp = 0.9648, RPD = 3.79 for TTC and Rc = 0.9893, Rp = 0.9878, RPD = 5.88 for CAP). Furthermore, the detection limit of 0.0001 µg/mL for both TTC and CAP was obtained. Finally, satisfactory (p > 0.05) results were obtained with the standard HPLC method. Therefore, SERS combined RF-PLS could be applied for fast, nondestructive sensing of TTC and CAP in milk.

Spectrophotometric determination of celecoxib and tramadol in the new approved formulated dosage form using principle component regression assistive model.

Celecoxib and tramadol have been combined in a novel FDA-approved medication to address acute pain disorders requiring opioid treatment when other analgesics proved either intolerable or ineffective. The absorbance spectra of celecoxib and tramadol exhibit significant overlap, posing challenges for their individual quantification. This study introduces a spectrophotometric quantification approach for celecoxib and tramadol using a principle component regression assistive model to assist resolving the overlapped spectra and quantifying both drugs in their binary mixture. The model was constructed by establishing calibration and validation sets for the celecoxib and tramadol mixture, employing a five-level, two-factor experimental design, resulting in 25 samples. Spectral data from these mixtures were measured and preprocessed to eliminate noise in the 200-210 nm range and zero absorbance values in the 290-400 nm range. Consequently, the dataset was streamlined to 81 variables. The predicted concentrations were compared with the known concentrations of celecoxib and tramadol, and the errors in the predictions were evidenced calculating root mean square error of cross-validation and root mean square error of prediction. Validation results demonstrate the efficacy of the models in predicting outcomes; recovery rates approaching 100 % are demonstrated with relative root mean square error of prediction (RRMSEP) values of 0.052 and 0.164 for tramadol and celecoxib, respectively. The selectivity was further evaluated by quantifying celecoxib and tramadol in the presence of potentially interfering drugs. The model demonstrated success in quantifying celecoxib and tramadol in laboratory-prepared tablets, producing metrics consistent with those reported in previously established spectrophotometric methods.

Protocol for measuring labile cytosolic Zn2+ using an in situ calibration of a genetically encoded FRET sensor.

Zinc (Zn2+) plays roles in structure, catalysis, and signaling. The majority of cellular Zn2+ is bound by proteins, but a fraction of total Zn2+ exists in a labile form. Here, we present a protocol for measuring labile cytosolic Zn2+ using an in situ calibration of a genetically encoded Förster resonance energy transfer (FRET) sensor. We describe steps for producing buffered Zn2+ solutions for performing an imaging-based calibration and analyzing the imaging data generated to determine labile Zn2+ concentration in single cells. For complete details on the use and execution of this protocol, please refer to Rakshit and Holtzen et al.1.

Effects of different observed datasets on the calibration of crop model parameters with GLUE: A case study using the CROPGRO-Soybean phenological model.

Suitable combinations of observed datasets for estimating crop model parameters can reduce the computational cost while ensuring accuracy. This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. Different combinations of four observed phenological stages, including initial flowering, initial pod, initial grain, and initial maturity stages for five soybean cultivars from Exp. 1 and Exp. 3 described in Table 2 are respectively used to calibrate the CSPs. The CSPM, driven by the optimized CSPs, is then evaluated against two independent phenological datasets from Exp. 2 and Exp. 4 described in Table 2. Root means square error (RMSE) (mean absolute error (MAE), coefficient of determination (R2), and Nash Sutcliffe model efficiency (NSE)) are 15.50 (14.63, 0.96, 0.42), 4.76 (3.92, 0.97, 0.95), 4.69 (3.72, 0.98, 0.95), 3.91 (3.40, 0.99, 0.96) and 12.54 (11.67, 0.95, 0.60), 5.07 (4.61, 0.98, 0.93), 4.97 (4.28, 0.97, 0.94), 4.58 (4.02, 0.98, 0.95) for using one, two, three, and four observed phenological stages in the CSPs estimation. The evaluation results suggest that RMSE and MAE decrease, and R2 and NSE increase with the increase in the number of observed phenological stages used for parameter calibration. However, there is no significant reduction in the RMSEs (MAEs, NSEs) using two, three, and four observed stages. Relatively reliable optimized CSPs for CSMP are obtained by using at least two observed phenological stages balancing calibration effect and computational cost. These findings provide new insight into parameter estimation of crop models.

[Adequate assessment of linear function of calibration according to GOST R ISO 11095-2007 for ethanol mass concentration measurement]. / Otsenka adekvatnosti lineinoi funktsii kalibrovki po GOST R ISO 11095-2007 dlya izmereniya massovoi kontsentratsii etanola.

OBJECTIVE: To assess the adequacy of linear function of calibration according to GOST R ISO 11095-2007 for ethanol mass concentration measurement using internal reference materials (RMs). MATERIAL AND METHODS: An experiment on calibration in accordance with the GOST R ISO 11095-2007 National standard of the RF was carried out using internal RMs, namely aqueous solutions of ethanol at different concentrations. Measurements were performed for two subbands of ethanol concentrations at RMs: 0.15-1.05 and 1.0-7.0 mg/ml - according to the certified methodology. RESULTS: The graphs of the calibration's functions based on experimental data are consistent with the assumption of the calibration function's linearity, as well as the assumption of the standard deviation's constance of residues is equitable for two subbands of RMs. CONCLUSION: Proven linear models in the calibration experiment may be recommended for use in the ethanol mass concentration measurement.

Single chamber Mg/Ca analyses of Globigerinoides ruber for paleo-proxy calibration using femtosecond LA-ICP-MS.

Mg/Ca is an independent proxy in paleoceanography to reconstruct past seawater temperature. Femtosecond Laser Ablation Inductively Coupled Plasma Mass Spectrometry (fs-LA-ICP-MS) was employed to determine the Mg/Ca composition of tests (shells) of the planktic foraminifer species Globigerinoides ruber albus (white chromotype) and G. ruber ruber (red/pink chromotype) sampled alive from the temperate to subtropical eastern North Atlantic with the research sailing yacht Eugen Seibold. Mg/Ca data are compared to (i) the measured in-situ temperature of ambient seawater, (ii) average mixed layer temperature, and (iii) sea surface temperature (SST). The pooled mean chamber Mg/Ca from each plankton tow site exhibits a positive relationship with SST. Two chamber-specific calibrations are derived, which are consistent with previous calibration equations for comparable paleo-archives. The results confirm fs-LA-ICP-MS as reliable method for determining Mg/Ca in G. ruber, and both the penultimate and antepenultimate chambers of adult specimens may provide comprehensible Mg/Ca temperatures of the surface ocean.

GPU-accelerated parallel image reconstruction strategies for magnetic particle imaging.

Objective. Image reconstruction is a fundamental step in magnetic particle imaging (MPI). One of the main challenges is the fact that the reconstructions are computationally intensive and time-consuming, so choosing an algorithm presents a compromise between accuracy and execution time, which depends on the application. This work proposes a method that provides both fast and accurate image reconstructions.Approach. Image reconstruction algorithms were implemented to be executed in parallel ingraphics processing units(GPUs) using the CUDA framework. The calculation of the model-based MPI calibration matrix was also implemented in GPU to allow both fast and flexible reconstructions.Main results. The parallel algorithms were able to accelerate the reconstructions by up to about6,100times in comparison to the serial Kaczmarz algorithm executed in the CPU, allowing for real-time applications. Reconstructions using the OpenMPIData dataset validated the proposed algorithms and demonstrated that they are able to provide both fast and accurate reconstructions. The calculation of the calibration matrix was accelerated by up to about 37 times.Significance. The parallel algorithms proposed in this work can provide single-frame MPI reconstructions in real time, with frame rates greater than 100 frames per second. The parallel calculation of the calibration matrix can be combined with the parallel reconstruction to deliver images in less time than the serial Kaczmarz reconstruction, potentially eliminating the need of storing the calibration matrix in the main memory, and providing the flexibility of redefining scanning and reconstruction parameters during execution.

Conductive block copolymer elastomers and psychophysical thresholding for accurate haptic effects.

Electrotactile stimulus is a form of sensory substitution in which an electrical signal is perceived as a mechanical sensation. The electrotactile effect could, in principle, recapitulate a range of tactile experience by selective activation of nerve endings. However, the method has been plagued by inconsistency, galvanic reactions, pain and desensitization, and unwanted stimulation of nontactile nerves. Here, we describe how a soft conductive block copolymer, a stretchable layout, and concentric electrodes, along with psychophysical thresholding, can circumvent these shortcomings. These purpose-designed materials, device layouts, and calibration techniques make it possible to generate accurate and reproducible sensations across a cohort of 10 human participants and to do so at ultralow currents (≥6 microamperes) without pain or desensitization. This material, form factor, and psychophysical approach could be useful for haptic devices and as a tool for activation of the peripheral nervous system.

Calibration algorithms for continuous glucose monitoring systems based on interstitial fluid sensing.

Continuous glucose monitoring (CGM) is of great importance to the treatment and prevention of diabetes. As a proven commercial technology, electrochemical glucose sensor based on interstitial fluid (ISF) sensing has high sensitivity and wide detection range. Therefore, it has good promotion prospects in noninvasive or minimally-invasive CGM system. However, since there are concentration differences and time lag between glucose in plasma and ISF, the accuracy of this type of sensors are still limited. Typical calibration algorithms rely on simple linear regression which do not account for the variability of the sensitivity of sensors. To enhance the accuracy and stability of CGM based on ISF, optimization of calibration algorithm for sensors is indispensable. While there have been considerable researches on improving calibration algorithms for CGM, they have still received less attention. This article reviews the problem of typical calibration and presents the outstanding calibration algorithms in recent years. Finally, combined with existing research and emerging sensing technologies, this paper makes an outlook on the future calibration algorithms for CGM sensors.

Understanding random resampling techniques for class imbalance correction and their consequences on calibration and discrimination of clinical risk prediction models.

OBJECTIVE: Class imbalance is sometimes considered a problem when developing clinical prediction models and assessing their performance. To address it, correction strategies involving manipulations of the training dataset, such as random undersampling or oversampling, are frequently used. The aim of this article is to illustrate the consequences of these class imbalance correction strategies on clinical prediction models' internal validity in terms of calibration and discrimination performances. METHODS: We used both heuristic intuition and formal mathematical reasoning to characterize the relations between conditional probabilities of interest and probabilities targeted when using random undersampling or oversampling. We propose a plug-in estimator that represents a natural correction for predictions obtained from models that have been trained on artificially balanced datasets ("naïve" models). We conducted a Monte Carlo simulation with two different data generation processes and present a real-world example using data from the International Stroke Trial database to empirically demonstrate the consequences of applying random resampling techniques for class imbalance correction on calibration and discrimination (in terms of Area Under the ROC, AUC) for logistic regression and tree-based prediction models. RESULTS: Across our simulations and in the real-world example, calibration of the naïve models was very poor. The models using the plug-in estimator generally outperformed the models relying on class imbalance correction in terms of calibration while achieving the same discrimination performance. CONCLUSION: Random resampling techniques for class imbalance correction do not generally improve discrimination performance (i.e., AUC), and their use is hard to justify when aiming at providing calibrated predictions. Improper use of such class imbalance correction techniques can lead to suboptimal data usage and less valid risk prediction models.